Overview

Dataset statistics

Number of variables31
Number of observations736
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory1.5 KiB

Variable types

Numeric7
Categorical19
Boolean5

Alerts

Anxiety is highly correlated with DepressionHigh correlation
Depression is highly correlated with AnxietyHigh correlation
Frequency [Hip hop] is highly correlated with Fav genre and 2 other fieldsHigh correlation
Frequency [Rap] is highly correlated with Fav genre and 2 other fieldsHigh correlation
Hours per day is highly correlated with While workingHigh correlation
While working is highly correlated with Hours per dayHigh correlation
Instrumentalist is highly correlated with ComposerHigh correlation
Composer is highly correlated with InstrumentalistHigh correlation
Fav genre is highly correlated with Frequency [Classical] and 13 other fieldsHigh correlation
Frequency [Classical] is highly correlated with Fav genreHigh correlation
Frequency [Country] is highly correlated with Fav genre and 1 other fieldsHigh correlation
Frequency [EDM] is highly correlated with Fav genreHigh correlation
Frequency [Folk] is highly correlated with Fav genre and 1 other fieldsHigh correlation
Frequency [Gospel] is highly correlated with Fav genreHigh correlation
Frequency [Jazz] is highly correlated with Fav genreHigh correlation
Frequency [K pop] is highly correlated with Fav genreHigh correlation
Frequency [Latin] is highly correlated with Frequency [R&B]High correlation
Frequency [Metal] is highly correlated with Fav genre and 1 other fieldsHigh correlation
Frequency [Pop] is highly correlated with Fav genre and 1 other fieldsHigh correlation
Frequency [R&B] is highly correlated with Fav genre and 4 other fieldsHigh correlation
Frequency [Rock] is highly correlated with Fav genre and 1 other fieldsHigh correlation
Frequency [Video game music] is highly correlated with Fav genreHigh correlation
Anxiety has 35 (4.8%) zeros Zeros
Depression has 84 (11.4%) zeros Zeros
Insomnia has 149 (20.2%) zeros Zeros
OCD has 248 (33.7%) zeros Zeros

Reproduction

Analysis started2023-03-15 15:33:37.301501
Analysis finished2023-03-15 15:34:00.830595
Duration23.53 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

Distinct33
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.91983696
Minimum10
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:01.025614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q118
median21
Q328
95-th percentile43
Maximum43
Range33
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.42156855
Coefficient of variation (CV)0.3520746636
Kurtosis0.1491324556
Mean23.91983696
Median Absolute Deviation (MAD)4
Skewness1.096459443
Sum17605
Variance70.92281684
MonotonicityNot monotonic
2023-03-15T20:34:01.213154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1885
 
11.5%
4363
 
8.6%
1961
 
8.3%
1759
 
8.0%
2153
 
7.2%
1644
 
6.0%
2040
 
5.4%
2239
 
5.3%
2337
 
5.0%
2522
 
3.0%
Other values (23)233
31.7%
ValueCountFrequency (%)
101
 
0.1%
123
 
0.4%
138
 
1.1%
1417
 
2.3%
1521
 
2.9%
1644
6.0%
1759
8.0%
1885
11.5%
1961
8.3%
2040
5.4%
ValueCountFrequency (%)
4363
8.6%
426
 
0.8%
414
 
0.5%
405
 
0.7%
391
 
0.1%
386
 
0.8%
375
 
0.7%
367
 
1.0%
357
 
1.0%
348
 
1.1%
Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size49.5 KiB
Spotify
459 
YouTube Music
94 
I do not use a streaming service.
71 
Apple Music
51 
Other streaming service
50 

Length

Max length33
Median length7
Mean length11.63858696
Min length7

Characters and Unicode

Total characters8566
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpotify
2nd rowPandora
3rd rowSpotify
4th rowYouTube Music
5th rowSpotify

Common Values

ValueCountFrequency (%)
Spotify459
62.4%
YouTube Music94
 
12.8%
I do not use a streaming service.71
 
9.6%
Apple Music51
 
6.9%
Other streaming service50
 
6.8%
Pandora11
 
1.5%

Length

2023-03-15T20:34:01.447257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:01.687289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
spotify459
32.6%
music145
 
10.3%
streaming121
 
8.6%
service121
 
8.6%
youtube94
 
6.7%
i71
 
5.0%
do71
 
5.0%
not71
 
5.0%
use71
 
5.0%
a71
 
5.0%
Other values (3)112
 
8.0%

Most occurring characters

ValueCountFrequency (%)
i846
 
9.9%
o706
 
8.2%
t701
 
8.2%
671
 
7.8%
e629
 
7.3%
p561
 
6.5%
S459
 
5.4%
f459
 
5.4%
y459
 
5.4%
s458
 
5.3%
Other values (20)2617
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6849
80.0%
Uppercase Letter975
 
11.4%
Space Separator671
 
7.8%
Other Punctuation71
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i846
12.4%
o706
10.3%
t701
10.2%
e629
9.2%
p561
8.2%
f459
 
6.7%
y459
 
6.7%
s458
 
6.7%
u404
 
5.9%
r303
 
4.4%
Other values (10)1323
19.3%
Uppercase Letter
ValueCountFrequency (%)
S459
47.1%
M145
 
14.9%
Y94
 
9.6%
T94
 
9.6%
I71
 
7.3%
A51
 
5.2%
O50
 
5.1%
P11
 
1.1%
Space Separator
ValueCountFrequency (%)
671
100.0%
Other Punctuation
ValueCountFrequency (%)
.71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7824
91.3%
Common742
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i846
 
10.8%
o706
 
9.0%
t701
 
9.0%
e629
 
8.0%
p561
 
7.2%
S459
 
5.9%
f459
 
5.9%
y459
 
5.9%
s458
 
5.9%
u404
 
5.2%
Other values (18)2142
27.4%
Common
ValueCountFrequency (%)
671
90.4%
.71
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII8566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i846
 
9.9%
o706
 
8.2%
t701
 
8.2%
671
 
7.8%
e629
 
7.3%
p561
 
6.5%
S459
 
5.4%
f459
 
5.4%
y459
 
5.4%
s458
 
5.3%
Other values (20)2617
30.6%

Hours per day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.406997283
Minimum0
Maximum9.5
Zeros6
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:01.910763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile9.5
Maximum9.5
Range9.5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.407887115
Coefficient of variation (CV)0.706747589
Kurtosis0.3949582335
Mean3.406997283
Median Absolute Deviation (MAD)1
Skewness1.076095337
Sum2507.55
Variance5.797920359
MonotonicityNot monotonic
2023-03-15T20:34:02.083309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2173
23.5%
3120
16.3%
1117
15.9%
483
11.3%
554
 
7.3%
647
 
6.4%
9.540
 
5.4%
829
 
3.9%
0.520
 
2.7%
1.517
 
2.3%
Other values (8)36
 
4.9%
ValueCountFrequency (%)
06
 
0.8%
0.11
 
0.1%
0.253
 
0.4%
0.520
 
2.7%
0.71
 
0.1%
1117
15.9%
1.517
 
2.3%
2173
23.5%
2.56
 
0.8%
3120
16.3%
ValueCountFrequency (%)
9.540
 
5.4%
93
 
0.4%
829
 
3.9%
715
 
2.0%
647
 
6.4%
554
7.3%
4.51
 
0.1%
483
11.3%
3120
16.3%
2.56
 
0.8%

While working
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size864.0 B
True
582 
False
154 
ValueCountFrequency (%)
True582
79.1%
False154
 
20.9%
2023-03-15T20:34:02.729350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Instrumentalist
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size864.0 B
False
501 
True
235 
ValueCountFrequency (%)
False501
68.1%
True235
31.9%
2023-03-15T20:34:02.897584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Composer
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size864.0 B
False
610 
True
126 
ValueCountFrequency (%)
False610
82.9%
True126
 
17.1%
2023-03-15T20:34:03.044596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Fav genre
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
Rock
188 
Pop
114 
Metal
88 
Classical
53 
Video game music
44 
Other values (11)
249 

Length

Max length16
Median length9
Mean length5.214673913
Min length3

Characters and Unicode

Total characters3838
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLatin
2nd rowRock
3rd rowVideo game music
4th rowJazz
5th rowR&B

Common Values

ValueCountFrequency (%)
Rock188
25.5%
Pop114
15.5%
Metal88
12.0%
Classical53
 
7.2%
Video game music44
 
6.0%
EDM37
 
5.0%
R&B35
 
4.8%
Hip hop35
 
4.8%
Folk30
 
4.1%
K pop26
 
3.5%
Other values (6)86
11.7%

Length

2023-03-15T20:34:03.168612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rock188
21.2%
pop140
15.8%
metal88
9.9%
classical53
 
6.0%
video44
 
5.0%
game44
 
5.0%
music44
 
5.0%
edm37
 
4.2%
hop35
 
4.0%
hip35
 
4.0%
Other values (9)177
20.0%

Most occurring characters

ValueCountFrequency (%)
o478
 
12.5%
c285
 
7.4%
a283
 
7.4%
p264
 
6.9%
R245
 
6.4%
l230
 
6.0%
k218
 
5.7%
i189
 
4.9%
e182
 
4.7%
s156
 
4.1%
Other values (26)1308
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2809
73.2%
Uppercase Letter845
 
22.0%
Space Separator149
 
3.9%
Other Punctuation35
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o478
17.0%
c285
10.1%
a283
10.1%
p264
9.4%
l230
8.2%
k218
7.8%
i189
 
6.7%
e182
 
6.5%
s156
 
5.6%
t116
 
4.1%
Other values (10)408
14.5%
Uppercase Letter
ValueCountFrequency (%)
R245
29.0%
M125
14.8%
P114
13.5%
C78
 
9.2%
V44
 
5.2%
E37
 
4.4%
D37
 
4.4%
H35
 
4.1%
B35
 
4.1%
F30
 
3.6%
Other values (4)65
 
7.7%
Space Separator
ValueCountFrequency (%)
149
100.0%
Other Punctuation
ValueCountFrequency (%)
&35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3654
95.2%
Common184
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o478
13.1%
c285
 
7.8%
a283
 
7.7%
p264
 
7.2%
R245
 
6.7%
l230
 
6.3%
k218
 
6.0%
i189
 
5.2%
e182
 
5.0%
s156
 
4.3%
Other values (24)1124
30.8%
Common
ValueCountFrequency (%)
149
81.0%
&35
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o478
 
12.5%
c285
 
7.4%
a283
 
7.4%
p264
 
6.9%
R245
 
6.4%
l230
 
6.0%
k218
 
5.7%
i189
 
4.9%
e182
 
4.7%
s156
 
4.1%
Other values (26)1308
34.1%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size864.0 B
True
525 
False
211 
ValueCountFrequency (%)
True525
71.3%
False211
28.7%
2023-03-15T20:34:03.309185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size864.0 B
True
408 
False
328 
ValueCountFrequency (%)
True408
55.4%
False328
44.6%
2023-03-15T20:34:03.461583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

BPM
Real number (ℝ≥0)

Distinct121
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.5658967
Minimum52.5
Maximum192.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:03.644102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum52.5
5-th percentile77
Q1105
median120
Q3140
95-th percentile180
Maximum192.5
Range140
Interquartile range (IQR)35

Descriptive statistics

Standard deviation29.58989656
Coefficient of variation (CV)0.2414203081
Kurtosis0.02091958986
Mean122.5658967
Median Absolute Deviation (MAD)17.5
Skewness0.2691991162
Sum90208.5
Variance875.5619782
MonotonicityNot monotonic
2023-03-15T20:34:03.880689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120152
 
20.7%
14025
 
3.4%
192.519
 
2.6%
15018
 
2.4%
11016
 
2.2%
10515
 
2.0%
13013
 
1.8%
9011
 
1.5%
12811
 
1.5%
13611
 
1.5%
Other values (111)445
60.5%
ValueCountFrequency (%)
52.58
1.1%
552
 
0.3%
561
 
0.1%
602
 
0.3%
611
 
0.1%
621
 
0.1%
631
 
0.1%
663
 
0.4%
681
 
0.1%
703
 
0.4%
ValueCountFrequency (%)
192.519
2.6%
1921
 
0.1%
1911
 
0.1%
1901
 
0.1%
1891
 
0.1%
1861
 
0.1%
1852
 
0.3%
1831
 
0.1%
1811
 
0.1%
18010
1.4%

Frequency [Classical]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.8 KiB
Rarely
259 
Sometimes
200 
Never
169 
Very frequently
108 

Length

Max length15
Median length9
Mean length7.90625
Min length5

Characters and Unicode

Total characters5819
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowSometimes
3rd rowNever
4th rowSometimes
5th rowNever

Common Values

ValueCountFrequency (%)
Rarely259
35.2%
Sometimes200
27.2%
Never169
23.0%
Very frequently108
14.7%

Length

2023-03-15T20:34:04.089907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:04.287265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
rarely259
30.7%
sometimes200
23.7%
never169
20.0%
very108
12.8%
frequently108
12.8%

Most occurring characters

ValueCountFrequency (%)
e1321
22.7%
r644
11.1%
y475
 
8.2%
m400
 
6.9%
l367
 
6.3%
t308
 
5.3%
R259
 
4.5%
a259
 
4.5%
s200
 
3.4%
i200
 
3.4%
Other values (10)1386
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4975
85.5%
Uppercase Letter736
 
12.6%
Space Separator108
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1321
26.6%
r644
12.9%
y475
 
9.5%
m400
 
8.0%
l367
 
7.4%
t308
 
6.2%
a259
 
5.2%
s200
 
4.0%
i200
 
4.0%
o200
 
4.0%
Other values (5)601
12.1%
Uppercase Letter
ValueCountFrequency (%)
R259
35.2%
S200
27.2%
N169
23.0%
V108
14.7%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5711
98.1%
Common108
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1321
23.1%
r644
11.3%
y475
 
8.3%
m400
 
7.0%
l367
 
6.4%
t308
 
5.4%
R259
 
4.5%
a259
 
4.5%
s200
 
3.5%
i200
 
3.5%
Other values (9)1278
22.4%
Common
ValueCountFrequency (%)
108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1321
22.7%
r644
11.1%
y475
 
8.2%
m400
 
6.9%
l367
 
6.3%
t308
 
5.3%
R259
 
4.5%
a259
 
4.5%
s200
 
3.4%
i200
 
3.4%
Other values (10)1386
23.8%

Frequency [Country]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
Never
343 
Rarely
233 
Sometimes
111 
Very frequently
49 

Length

Max length15
Median length9
Mean length6.585597826
Min length5

Characters and Unicode

Total characters4847
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowNever
3rd rowNever
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Never343
46.6%
Rarely233
31.7%
Sometimes111
 
15.1%
Very frequently49
 
6.7%

Length

2023-03-15T20:34:04.501666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:04.733111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never343
43.7%
rarely233
29.7%
sometimes111
 
14.1%
very49
 
6.2%
frequently49
 
6.2%

Most occurring characters

ValueCountFrequency (%)
e1288
26.6%
r674
13.9%
N343
 
7.1%
v343
 
7.1%
y331
 
6.8%
l282
 
5.8%
R233
 
4.8%
a233
 
4.8%
m222
 
4.6%
t160
 
3.3%
Other values (10)738
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4062
83.8%
Uppercase Letter736
 
15.2%
Space Separator49
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1288
31.7%
r674
16.6%
v343
 
8.4%
y331
 
8.1%
l282
 
6.9%
a233
 
5.7%
m222
 
5.5%
t160
 
3.9%
o111
 
2.7%
i111
 
2.7%
Other values (5)307
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
N343
46.6%
R233
31.7%
S111
 
15.1%
V49
 
6.7%
Space Separator
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4798
99.0%
Common49
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1288
26.8%
r674
14.0%
N343
 
7.1%
v343
 
7.1%
y331
 
6.9%
l282
 
5.9%
R233
 
4.9%
a233
 
4.9%
m222
 
4.6%
t160
 
3.3%
Other values (9)689
14.4%
Common
ValueCountFrequency (%)
49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1288
26.6%
r674
13.9%
N343
 
7.1%
v343
 
7.1%
y331
 
6.8%
l282
 
5.8%
R233
 
4.8%
a233
 
4.8%
m222
 
4.6%
t160
 
3.3%
Other values (10)738
15.2%

Frequency [EDM]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.3 KiB
Never
307 
Rarely
194 
Sometimes
146 
Very frequently
89 

Length

Max length15
Median length9
Mean length7.266304348
Min length5

Characters and Unicode

Total characters5348
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowNever
3rd rowVery frequently
4th rowNever
5th rowRarely

Common Values

ValueCountFrequency (%)
Never307
41.7%
Rarely194
26.4%
Sometimes146
19.8%
Very frequently89
 
12.1%

Length

2023-03-15T20:34:04.969680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:05.140692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never307
37.2%
rarely194
23.5%
sometimes146
17.7%
very89
 
10.8%
frequently89
 
10.8%

Most occurring characters

ValueCountFrequency (%)
e1367
25.6%
r679
12.7%
y372
 
7.0%
N307
 
5.7%
v307
 
5.7%
m292
 
5.5%
l283
 
5.3%
t235
 
4.4%
a194
 
3.6%
R194
 
3.6%
Other values (10)1118
20.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4523
84.6%
Uppercase Letter736
 
13.8%
Space Separator89
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1367
30.2%
r679
15.0%
y372
 
8.2%
v307
 
6.8%
m292
 
6.5%
l283
 
6.3%
t235
 
5.2%
a194
 
4.3%
o146
 
3.2%
i146
 
3.2%
Other values (5)502
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
N307
41.7%
R194
26.4%
S146
19.8%
V89
 
12.1%
Space Separator
ValueCountFrequency (%)
89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5259
98.3%
Common89
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1367
26.0%
r679
12.9%
y372
 
7.1%
N307
 
5.8%
v307
 
5.8%
m292
 
5.6%
l283
 
5.4%
t235
 
4.5%
a194
 
3.7%
R194
 
3.7%
Other values (9)1029
19.6%
Common
ValueCountFrequency (%)
89
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1367
25.6%
r679
12.7%
y372
 
7.0%
N307
 
5.7%
v307
 
5.7%
m292
 
5.5%
l283
 
5.3%
t235
 
4.4%
a194
 
3.6%
R194
 
3.6%
Other values (10)1118
20.9%

Frequency [Folk]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
Never
292 
Rarely
221 
Sometimes
145 
Very frequently
78 

Length

Max length15
Median length9
Mean length7.148097826
Min length5

Characters and Unicode

Total characters5261
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowRarely
3rd rowNever
4th rowRarely
5th rowNever

Common Values

ValueCountFrequency (%)
Never292
39.7%
Rarely221
30.0%
Sometimes145
19.7%
Very frequently78
 
10.6%

Length

2023-03-15T20:34:05.313705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:05.529704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never292
35.9%
rarely221
27.1%
sometimes145
17.8%
very78
 
9.6%
frequently78
 
9.6%

Most occurring characters

ValueCountFrequency (%)
e1329
25.3%
r669
12.7%
y377
 
7.2%
l299
 
5.7%
N292
 
5.6%
v292
 
5.6%
m290
 
5.5%
t223
 
4.2%
a221
 
4.2%
R221
 
4.2%
Other values (10)1048
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4447
84.5%
Uppercase Letter736
 
14.0%
Space Separator78
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1329
29.9%
r669
15.0%
y377
 
8.5%
l299
 
6.7%
v292
 
6.6%
m290
 
6.5%
t223
 
5.0%
a221
 
5.0%
o145
 
3.3%
i145
 
3.3%
Other values (5)457
 
10.3%
Uppercase Letter
ValueCountFrequency (%)
N292
39.7%
R221
30.0%
S145
19.7%
V78
 
10.6%
Space Separator
ValueCountFrequency (%)
78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5183
98.5%
Common78
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1329
25.6%
r669
12.9%
y377
 
7.3%
l299
 
5.8%
N292
 
5.6%
v292
 
5.6%
m290
 
5.6%
t223
 
4.3%
a221
 
4.3%
R221
 
4.3%
Other values (9)970
18.7%
Common
ValueCountFrequency (%)
78
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1329
25.3%
r669
12.7%
y377
 
7.2%
l299
 
5.7%
N292
 
5.6%
v292
 
5.6%
m290
 
5.5%
t223
 
4.2%
a221
 
4.2%
R221
 
4.2%
Other values (10)1048
19.9%

Frequency [Gospel]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
Never
535 
Rarely
135 
Sometimes
 
52
Very frequently
 
14

Length

Max length15
Median length5
Mean length5.65625
Min length5

Characters and Unicode

Total characters4163
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowSometimes
3rd rowNever
4th rowSometimes
5th rowRarely

Common Values

ValueCountFrequency (%)
Never535
72.7%
Rarely135
 
18.3%
Sometimes52
 
7.1%
Very frequently14
 
1.9%

Length

2023-03-15T20:34:05.781629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:06.071104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never535
71.3%
rarely135
 
18.0%
sometimes52
 
6.9%
very14
 
1.9%
frequently14
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e1351
32.5%
r698
16.8%
N535
 
12.9%
v535
 
12.9%
y163
 
3.9%
l149
 
3.6%
R135
 
3.2%
a135
 
3.2%
m104
 
2.5%
t66
 
1.6%
Other values (10)292
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3413
82.0%
Uppercase Letter736
 
17.7%
Space Separator14
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1351
39.6%
r698
20.5%
v535
 
15.7%
y163
 
4.8%
l149
 
4.4%
a135
 
4.0%
m104
 
3.0%
t66
 
1.9%
o52
 
1.5%
i52
 
1.5%
Other values (5)108
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
N535
72.7%
R135
 
18.3%
S52
 
7.1%
V14
 
1.9%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4149
99.7%
Common14
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1351
32.6%
r698
16.8%
N535
 
12.9%
v535
 
12.9%
y163
 
3.9%
l149
 
3.6%
R135
 
3.3%
a135
 
3.3%
m104
 
2.5%
t66
 
1.6%
Other values (9)278
 
6.7%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1351
32.5%
r698
16.8%
N535
 
12.9%
v535
 
12.9%
y163
 
3.9%
l149
 
3.6%
R135
 
3.2%
a135
 
3.2%
m104
 
2.5%
t66
 
1.6%
Other values (10)292
 
7.0%

Frequency [Hip hop]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
Sometimes
218 
Rarely
214 
Never
181 
Very frequently
123 

Length

Max length15
Median length9
Mean length8.14673913
Min length5

Characters and Unicode

Total characters5996
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowRarely
3rd rowRarely
4th rowNever
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Sometimes218
29.6%
Rarely214
29.1%
Never181
24.6%
Very frequently123
16.7%

Length

2023-03-15T20:34:06.278486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:06.492956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sometimes218
25.4%
rarely214
24.9%
never181
21.1%
very123
14.3%
frequently123
14.3%

Most occurring characters

ValueCountFrequency (%)
e1381
23.0%
r641
10.7%
y460
 
7.7%
m436
 
7.3%
t341
 
5.7%
l337
 
5.6%
o218
 
3.6%
S218
 
3.6%
s218
 
3.6%
i218
 
3.6%
Other values (10)1528
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5137
85.7%
Uppercase Letter736
 
12.3%
Space Separator123
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1381
26.9%
r641
12.5%
y460
 
9.0%
m436
 
8.5%
t341
 
6.6%
l337
 
6.6%
o218
 
4.2%
s218
 
4.2%
i218
 
4.2%
a214
 
4.2%
Other values (5)673
13.1%
Uppercase Letter
ValueCountFrequency (%)
S218
29.6%
R214
29.1%
N181
24.6%
V123
16.7%
Space Separator
ValueCountFrequency (%)
123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5873
97.9%
Common123
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1381
23.5%
r641
10.9%
y460
 
7.8%
m436
 
7.4%
t341
 
5.8%
l337
 
5.7%
o218
 
3.7%
S218
 
3.7%
s218
 
3.7%
i218
 
3.7%
Other values (9)1405
23.9%
Common
ValueCountFrequency (%)
123
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1381
23.0%
r641
10.7%
y460
 
7.7%
m436
 
7.3%
t341
 
5.7%
l337
 
5.6%
o218
 
3.6%
S218
 
3.6%
s218
 
3.6%
i218
 
3.6%
Other values (10)1528
25.5%

Frequency [Jazz]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.1 KiB
Never
261 
Rarely
247 
Sometimes
175 
Very frequently
53 

Length

Max length15
Median length9
Mean length7.006793478
Min length5

Characters and Unicode

Total characters5157
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowVery frequently
3rd rowRarely
4th rowVery frequently
5th rowNever

Common Values

ValueCountFrequency (%)
Never261
35.5%
Rarely247
33.6%
Sometimes175
23.8%
Very frequently53
 
7.2%

Length

2023-03-15T20:34:06.669971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:06.838983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never261
33.1%
rarely247
31.3%
sometimes175
22.2%
very53
 
6.7%
frequently53
 
6.7%

Most occurring characters

ValueCountFrequency (%)
e1278
24.8%
r614
11.9%
y353
 
6.8%
m350
 
6.8%
l300
 
5.8%
N261
 
5.1%
v261
 
5.1%
a247
 
4.8%
R247
 
4.8%
t228
 
4.4%
Other values (10)1018
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4368
84.7%
Uppercase Letter736
 
14.3%
Space Separator53
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1278
29.3%
r614
14.1%
y353
 
8.1%
m350
 
8.0%
l300
 
6.9%
v261
 
6.0%
a247
 
5.7%
t228
 
5.2%
o175
 
4.0%
i175
 
4.0%
Other values (5)387
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
N261
35.5%
R247
33.6%
S175
23.8%
V53
 
7.2%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5104
99.0%
Common53
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1278
25.0%
r614
12.0%
y353
 
6.9%
m350
 
6.9%
l300
 
5.9%
N261
 
5.1%
v261
 
5.1%
a247
 
4.8%
R247
 
4.8%
t228
 
4.5%
Other values (9)965
18.9%
Common
ValueCountFrequency (%)
53
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1278
24.8%
r614
11.9%
y353
 
6.8%
m350
 
6.8%
l300
 
5.8%
N261
 
5.1%
v261
 
5.1%
a247
 
4.8%
R247
 
4.8%
t228
 
4.4%
Other values (10)1018
19.7%

Frequency [K pop]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size45.9 KiB
Never
416 
Rarely
176 
Very frequently
77 
Sometimes
67 

Length

Max length15
Median length5
Mean length6.649456522
Min length5

Characters and Unicode

Total characters4894
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowRarely
3rd rowVery frequently
4th rowSometimes
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Never416
56.5%
Rarely176
23.9%
Very frequently77
 
10.5%
Sometimes67
 
9.1%

Length

2023-03-15T20:34:06.976993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:07.144005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never416
51.2%
rarely176
21.6%
very77
 
9.5%
frequently77
 
9.5%
sometimes67
 
8.2%

Most occurring characters

ValueCountFrequency (%)
e1373
28.1%
r746
15.2%
N416
 
8.5%
v416
 
8.5%
y330
 
6.7%
l253
 
5.2%
R176
 
3.6%
a176
 
3.6%
t144
 
2.9%
m134
 
2.7%
Other values (10)730
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4081
83.4%
Uppercase Letter736
 
15.0%
Space Separator77
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1373
33.6%
r746
18.3%
v416
 
10.2%
y330
 
8.1%
l253
 
6.2%
a176
 
4.3%
t144
 
3.5%
m134
 
3.3%
u77
 
1.9%
n77
 
1.9%
Other values (5)355
 
8.7%
Uppercase Letter
ValueCountFrequency (%)
N416
56.5%
R176
23.9%
V77
 
10.5%
S67
 
9.1%
Space Separator
ValueCountFrequency (%)
77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4817
98.4%
Common77
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1373
28.5%
r746
15.5%
N416
 
8.6%
v416
 
8.6%
y330
 
6.9%
l253
 
5.3%
R176
 
3.7%
a176
 
3.7%
t144
 
3.0%
m134
 
2.8%
Other values (9)653
13.6%
Common
ValueCountFrequency (%)
77
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1373
28.1%
r746
15.2%
N416
 
8.5%
v416
 
8.5%
y330
 
6.7%
l253
 
5.2%
R176
 
3.6%
a176
 
3.6%
t144
 
2.9%
m134
 
2.7%
Other values (10)730
14.9%

Frequency [Latin]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size45.5 KiB
Never
443 
Rarely
172 
Sometimes
88 
Very frequently
 
33

Length

Max length15
Median length5
Mean length6.160326087
Min length5

Characters and Unicode

Total characters4534
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowSometimes
3rd rowNever
4th rowVery frequently
5th rowSometimes

Common Values

ValueCountFrequency (%)
Never443
60.2%
Rarely172
 
23.4%
Sometimes88
 
12.0%
Very frequently33
 
4.5%

Length

2023-03-15T20:34:07.344118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:07.531660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never443
57.6%
rarely172
 
22.4%
sometimes88
 
11.4%
very33
 
4.3%
frequently33
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e1333
29.4%
r681
15.0%
N443
 
9.8%
v443
 
9.8%
y238
 
5.2%
l205
 
4.5%
m176
 
3.9%
a172
 
3.8%
R172
 
3.8%
t121
 
2.7%
Other values (10)550
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3765
83.0%
Uppercase Letter736
 
16.2%
Space Separator33
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1333
35.4%
r681
18.1%
v443
 
11.8%
y238
 
6.3%
l205
 
5.4%
m176
 
4.7%
a172
 
4.6%
t121
 
3.2%
o88
 
2.3%
i88
 
2.3%
Other values (5)220
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
N443
60.2%
R172
 
23.4%
S88
 
12.0%
V33
 
4.5%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4501
99.3%
Common33
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1333
29.6%
r681
15.1%
N443
 
9.8%
v443
 
9.8%
y238
 
5.3%
l205
 
4.6%
m176
 
3.9%
a172
 
3.8%
R172
 
3.8%
t121
 
2.7%
Other values (9)517
 
11.5%
Common
ValueCountFrequency (%)
33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1333
29.4%
r681
15.0%
N443
 
9.8%
v443
 
9.8%
y238
 
5.2%
l205
 
4.5%
m176
 
3.9%
a172
 
3.8%
R172
 
3.8%
t121
 
2.7%
Other values (10)550
12.1%

Frequency [Lofi]
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.3 KiB
Never
280 
Rarely
211 
Sometimes
160 
Very frequently
85 

Length

Max length15
Median length9
Mean length7.311141304
Min length5

Characters and Unicode

Total characters5381
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowRarely
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Never280
38.0%
Rarely211
28.7%
Sometimes160
21.7%
Very frequently85
 
11.5%

Length

2023-03-15T20:34:07.673670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:07.834683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never280
34.1%
rarely211
25.7%
sometimes160
19.5%
very85
 
10.4%
frequently85
 
10.4%

Most occurring characters

ValueCountFrequency (%)
e1346
25.0%
r661
12.3%
y381
 
7.1%
m320
 
5.9%
l296
 
5.5%
N280
 
5.2%
v280
 
5.2%
t245
 
4.6%
a211
 
3.9%
R211
 
3.9%
Other values (10)1150
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4560
84.7%
Uppercase Letter736
 
13.7%
Space Separator85
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1346
29.5%
r661
14.5%
y381
 
8.4%
m320
 
7.0%
l296
 
6.5%
v280
 
6.1%
t245
 
5.4%
a211
 
4.6%
o160
 
3.5%
i160
 
3.5%
Other values (5)500
 
11.0%
Uppercase Letter
ValueCountFrequency (%)
N280
38.0%
R211
28.7%
S160
21.7%
V85
 
11.5%
Space Separator
ValueCountFrequency (%)
85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5296
98.4%
Common85
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1346
25.4%
r661
12.5%
y381
 
7.2%
m320
 
6.0%
l296
 
5.6%
N280
 
5.3%
v280
 
5.3%
t245
 
4.6%
a211
 
4.0%
R211
 
4.0%
Other values (9)1065
20.1%
Common
ValueCountFrequency (%)
85
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1346
25.0%
r661
12.3%
y381
 
7.1%
m320
 
5.9%
l296
 
5.5%
N280
 
5.2%
v280
 
5.2%
t245
 
4.6%
a211
 
3.9%
R211
 
3.9%
Other values (10)1150
21.4%

Frequency [Metal]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.8 KiB
Never
264 
Rarely
192 
Very frequently
146 
Sometimes
134 

Length

Max length15
Median length9
Mean length7.972826087
Min length5

Characters and Unicode

Total characters5868
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowNever
3rd rowSometimes
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Never264
35.9%
Rarely192
26.1%
Very frequently146
19.8%
Sometimes134
18.2%

Length

2023-03-15T20:34:08.041278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:08.256261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never264
29.9%
rarely192
21.8%
very146
16.6%
frequently146
16.6%
sometimes134
15.2%

Most occurring characters

ValueCountFrequency (%)
e1426
24.3%
r748
12.7%
y484
 
8.2%
l338
 
5.8%
t280
 
4.8%
m268
 
4.6%
N264
 
4.5%
v264
 
4.5%
R192
 
3.3%
a192
 
3.3%
Other values (10)1412
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4986
85.0%
Uppercase Letter736
 
12.5%
Space Separator146
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1426
28.6%
r748
15.0%
y484
 
9.7%
l338
 
6.8%
t280
 
5.6%
m268
 
5.4%
v264
 
5.3%
a192
 
3.9%
u146
 
2.9%
n146
 
2.9%
Other values (5)694
13.9%
Uppercase Letter
ValueCountFrequency (%)
N264
35.9%
R192
26.1%
V146
19.8%
S134
18.2%
Space Separator
ValueCountFrequency (%)
146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5722
97.5%
Common146
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1426
24.9%
r748
13.1%
y484
 
8.5%
l338
 
5.9%
t280
 
4.9%
m268
 
4.7%
N264
 
4.6%
v264
 
4.6%
R192
 
3.4%
a192
 
3.4%
Other values (9)1266
22.1%
Common
ValueCountFrequency (%)
146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1426
24.3%
r748
12.7%
y484
 
8.2%
l338
 
5.8%
t280
 
4.8%
m268
 
4.6%
N264
 
4.5%
v264
 
4.5%
R192
 
3.3%
a192
 
3.3%
Other values (10)1412
24.1%

Frequency [Pop]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
Very frequently
277 
Sometimes
261 
Rarely
142 
Never
56 

Length

Max length15
Median length9
Mean length10.375
Min length5

Characters and Unicode

Total characters7636
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowSometimes
3rd rowRarely
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Very frequently277
37.6%
Sometimes261
35.5%
Rarely142
19.3%
Never56
 
7.6%

Length

2023-03-15T20:34:08.424338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:08.603325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
very277
27.3%
frequently277
27.3%
sometimes261
25.8%
rarely142
14.0%
never56
 
5.5%

Most occurring characters

ValueCountFrequency (%)
e1607
21.0%
r752
 
9.8%
y696
 
9.1%
t538
 
7.0%
m522
 
6.8%
l419
 
5.5%
n277
 
3.6%
V277
 
3.6%
u277
 
3.6%
q277
 
3.6%
Other values (10)1994
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6623
86.7%
Uppercase Letter736
 
9.6%
Space Separator277
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1607
24.3%
r752
11.4%
y696
10.5%
t538
 
8.1%
m522
 
7.9%
l419
 
6.3%
n277
 
4.2%
u277
 
4.2%
q277
 
4.2%
f277
 
4.2%
Other values (5)981
14.8%
Uppercase Letter
ValueCountFrequency (%)
V277
37.6%
S261
35.5%
R142
19.3%
N56
 
7.6%
Space Separator
ValueCountFrequency (%)
277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7359
96.4%
Common277
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1607
21.8%
r752
10.2%
y696
9.5%
t538
 
7.3%
m522
 
7.1%
l419
 
5.7%
n277
 
3.8%
V277
 
3.8%
u277
 
3.8%
q277
 
3.8%
Other values (9)1717
23.3%
Common
ValueCountFrequency (%)
277
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1607
21.0%
r752
 
9.8%
y696
 
9.1%
t538
 
7.0%
m522
 
6.8%
l419
 
5.5%
n277
 
3.6%
V277
 
3.6%
u277
 
3.6%
q277
 
3.6%
Other values (10)1994
26.1%

Frequency [R&B]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.7 KiB
Never
225 
Rarely
211 
Sometimes
184 
Very frequently
116 

Length

Max length15
Median length9
Mean length7.862771739
Min length5

Characters and Unicode

Total characters5787
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowSometimes
3rd rowNever
4th rowSometimes
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Never225
30.6%
Rarely211
28.7%
Sometimes184
25.0%
Very frequently116
15.8%

Length

2023-03-15T20:34:08.790716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:08.977506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never225
26.4%
rarely211
24.8%
sometimes184
21.6%
very116
13.6%
frequently116
13.6%

Most occurring characters

ValueCountFrequency (%)
e1377
23.8%
r668
11.5%
y443
 
7.7%
m368
 
6.4%
l327
 
5.7%
t300
 
5.2%
N225
 
3.9%
v225
 
3.9%
a211
 
3.6%
R211
 
3.6%
Other values (10)1432
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4935
85.3%
Uppercase Letter736
 
12.7%
Space Separator116
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1377
27.9%
r668
13.5%
y443
 
9.0%
m368
 
7.5%
l327
 
6.6%
t300
 
6.1%
v225
 
4.6%
a211
 
4.3%
o184
 
3.7%
i184
 
3.7%
Other values (5)648
13.1%
Uppercase Letter
ValueCountFrequency (%)
N225
30.6%
R211
28.7%
S184
25.0%
V116
15.8%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5671
98.0%
Common116
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1377
24.3%
r668
11.8%
y443
 
7.8%
m368
 
6.5%
l327
 
5.8%
t300
 
5.3%
N225
 
4.0%
v225
 
4.0%
a211
 
3.7%
R211
 
3.7%
Other values (9)1316
23.2%
Common
ValueCountFrequency (%)
116
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1377
23.8%
r668
11.5%
y443
 
7.7%
m368
 
6.4%
l327
 
5.7%
t300
 
5.2%
N225
 
3.9%
v225
 
3.9%
a211
 
3.6%
R211
 
3.6%
Other values (10)1432
24.7%

Frequency [Rap]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
Rarely
215 
Never
200 
Sometimes
195 
Very frequently
126 

Length

Max length15
Median length9
Mean length8.063858696
Min length5

Characters and Unicode

Total characters5935
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery frequently
2nd rowRarely
3rd rowRarely
4th rowNever
5th rowVery frequently

Common Values

ValueCountFrequency (%)
Rarely215
29.2%
Never200
27.2%
Sometimes195
26.5%
Very frequently126
17.1%

Length

2023-03-15T20:34:09.136223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:09.302131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
rarely215
24.9%
never200
23.2%
sometimes195
22.6%
very126
14.6%
frequently126
14.6%

Most occurring characters

ValueCountFrequency (%)
e1383
23.3%
r667
11.2%
y467
 
7.9%
m390
 
6.6%
l341
 
5.7%
t321
 
5.4%
a215
 
3.6%
R215
 
3.6%
v200
 
3.4%
N200
 
3.4%
Other values (10)1536
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5073
85.5%
Uppercase Letter736
 
12.4%
Space Separator126
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1383
27.3%
r667
13.1%
y467
 
9.2%
m390
 
7.7%
l341
 
6.7%
t321
 
6.3%
a215
 
4.2%
v200
 
3.9%
o195
 
3.8%
i195
 
3.8%
Other values (5)699
13.8%
Uppercase Letter
ValueCountFrequency (%)
R215
29.2%
N200
27.2%
S195
26.5%
V126
17.1%
Space Separator
ValueCountFrequency (%)
126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5809
97.9%
Common126
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1383
23.8%
r667
11.5%
y467
 
8.0%
m390
 
6.7%
l341
 
5.9%
t321
 
5.5%
a215
 
3.7%
R215
 
3.7%
v200
 
3.4%
N200
 
3.4%
Other values (9)1410
24.3%
Common
ValueCountFrequency (%)
126
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1383
23.3%
r667
11.2%
y467
 
7.9%
m390
 
6.6%
l341
 
5.7%
t321
 
5.4%
a215
 
3.6%
R215
 
3.6%
v200
 
3.4%
N200
 
3.4%
Other values (10)1536
25.9%

Frequency [Rock]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size48.9 KiB
Very frequently
330 
Sometimes
219 
Rarely
96 
Never
91 

Length

Max length15
Median length9
Mean length10.80434783
Min length5

Characters and Unicode

Total characters7952
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowVery frequently
3rd rowRarely
4th rowNever
5th rowNever

Common Values

ValueCountFrequency (%)
Very frequently330
44.8%
Sometimes219
29.8%
Rarely96
 
13.0%
Never91
 
12.4%

Length

2023-03-15T20:34:09.487549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:09.700233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
very330
31.0%
frequently330
31.0%
sometimes219
20.5%
rarely96
 
9.0%
never91
 
8.5%

Most occurring characters

ValueCountFrequency (%)
e1706
21.5%
r847
10.7%
y756
 
9.5%
t549
 
6.9%
m438
 
5.5%
l426
 
5.4%
n330
 
4.1%
V330
 
4.1%
u330
 
4.1%
q330
 
4.1%
Other values (10)1910
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6886
86.6%
Uppercase Letter736
 
9.3%
Space Separator330
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1706
24.8%
r847
12.3%
y756
11.0%
t549
 
8.0%
m438
 
6.4%
l426
 
6.2%
n330
 
4.8%
u330
 
4.8%
q330
 
4.8%
f330
 
4.8%
Other values (5)844
12.3%
Uppercase Letter
ValueCountFrequency (%)
V330
44.8%
S219
29.8%
R96
 
13.0%
N91
 
12.4%
Space Separator
ValueCountFrequency (%)
330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7622
95.9%
Common330
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1706
22.4%
r847
11.1%
y756
9.9%
t549
 
7.2%
m438
 
5.7%
l426
 
5.6%
n330
 
4.3%
V330
 
4.3%
u330
 
4.3%
q330
 
4.3%
Other values (9)1580
20.7%
Common
ValueCountFrequency (%)
330
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1706
21.5%
r847
10.7%
y756
 
9.5%
t549
 
6.9%
m438
 
5.5%
l426
 
5.4%
n330
 
4.1%
V330
 
4.1%
u330
 
4.1%
q330
 
4.1%
Other values (10)1910
24.0%

Frequency [Video game music]
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size46.7 KiB
Never
236 
Rarely
197 
Sometimes
186 
Very frequently
117 

Length

Max length15
Median length9
Mean length7.868206522
Min length5

Characters and Unicode

Total characters5791
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowRarely
3rd rowVery frequently
4th rowNever
5th rowRarely

Common Values

ValueCountFrequency (%)
Never236
32.1%
Rarely197
26.8%
Sometimes186
25.3%
Very frequently117
15.9%

Length

2023-03-15T20:34:09.841243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:09.998256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
never236
27.7%
rarely197
23.1%
sometimes186
21.8%
very117
13.7%
frequently117
13.7%

Most occurring characters

ValueCountFrequency (%)
e1392
24.0%
r667
11.5%
y431
 
7.4%
m372
 
6.4%
l314
 
5.4%
t303
 
5.2%
N236
 
4.1%
v236
 
4.1%
a197
 
3.4%
R197
 
3.4%
Other values (10)1446
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4938
85.3%
Uppercase Letter736
 
12.7%
Space Separator117
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1392
28.2%
r667
13.5%
y431
 
8.7%
m372
 
7.5%
l314
 
6.4%
t303
 
6.1%
v236
 
4.8%
a197
 
4.0%
o186
 
3.8%
i186
 
3.8%
Other values (5)654
13.2%
Uppercase Letter
ValueCountFrequency (%)
N236
32.1%
R197
26.8%
S186
25.3%
V117
15.9%
Space Separator
ValueCountFrequency (%)
117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5674
98.0%
Common117
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1392
24.5%
r667
11.8%
y431
 
7.6%
m372
 
6.6%
l314
 
5.5%
t303
 
5.3%
N236
 
4.2%
v236
 
4.2%
a197
 
3.5%
R197
 
3.5%
Other values (9)1329
23.4%
Common
ValueCountFrequency (%)
117
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1392
24.0%
r667
11.5%
y431
 
7.4%
m372
 
6.4%
l314
 
5.4%
t303
 
5.2%
N236
 
4.1%
v236
 
4.1%
a197
 
3.4%
R197
 
3.4%
Other values (10)1446
25.0%

Anxiety
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.83763587
Minimum0
Maximum10
Zeros35
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:10.155609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.793054429
Coefficient of variation (CV)0.4784564319
Kurtosis-0.7657910725
Mean5.83763587
Median Absolute Deviation (MAD)2
Skewness-0.4213499709
Sum4296.5
Variance7.801153043
MonotonicityNot monotonic
2023-03-15T20:34:10.582214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7122
16.6%
8115
15.6%
683
11.3%
369
9.4%
1067
9.1%
559
8.0%
956
7.6%
456
7.6%
244
 
6.0%
035
 
4.8%
Other values (2)30
 
4.1%
ValueCountFrequency (%)
035
 
4.8%
129
 
3.9%
244
 
6.0%
369
9.4%
456
7.6%
559
8.0%
683
11.3%
7122
16.6%
7.51
 
0.1%
8115
15.6%
ValueCountFrequency (%)
1067
9.1%
956
7.6%
8115
15.6%
7.51
 
0.1%
7122
16.6%
683
11.3%
559
8.0%
456
7.6%
369
9.4%
244
 
6.0%

Depression
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.796195652
Minimum0
Maximum10
Zeros84
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:10.772580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.02887001
Coefficient of variation (CV)0.6315151068
Kurtosis-1.145947361
Mean4.796195652
Median Absolute Deviation (MAD)3
Skewness-0.04844887327
Sum3530
Variance9.174053534
MonotonicityNot monotonic
2023-03-15T20:34:10.899584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
796
13.0%
293
12.6%
688
12.0%
084
11.4%
877
10.5%
359
8.0%
458
7.9%
556
7.6%
1045
6.1%
140
5.4%
Other values (2)40
5.4%
ValueCountFrequency (%)
084
11.4%
140
5.4%
293
12.6%
359
8.0%
3.52
 
0.3%
458
7.9%
556
7.6%
688
12.0%
796
13.0%
877
10.5%
ValueCountFrequency (%)
1045
6.1%
938
 
5.2%
877
10.5%
796
13.0%
688
12.0%
556
7.6%
458
7.9%
3.52
 
0.3%
359
8.0%
293
12.6%

Insomnia
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.738451087
Minimum0
Maximum10
Zeros149
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:11.037577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.088689446
Coefficient of variation (CV)0.82619496
Kurtosis-1.021272416
Mean3.738451087
Median Absolute Deviation (MAD)3
Skewness0.4164553801
Sum2751.5
Variance9.540002496
MonotonicityNot monotonic
2023-03-15T20:34:11.179589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0149
20.2%
288
12.0%
182
11.1%
368
9.2%
662
8.4%
759
 
8.0%
459
 
8.0%
558
 
7.9%
849
 
6.7%
1034
 
4.6%
Other values (2)28
 
3.8%
ValueCountFrequency (%)
0149
20.2%
182
11.1%
288
12.0%
368
9.2%
3.51
 
0.1%
459
 
8.0%
558
 
7.9%
662
8.4%
759
 
8.0%
849
 
6.7%
ValueCountFrequency (%)
1034
 
4.6%
927
 
3.7%
849
6.7%
759
8.0%
662
8.4%
558
7.9%
459
8.0%
3.51
 
0.1%
368
9.2%
288
12.0%

OCD
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.637228261
Minimum0
Maximum10
Zeros248
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-03-15T20:34:11.355602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.842017092
Coefficient of variation (CV)1.077653055
Kurtosis-0.1273238933
Mean2.637228261
Median Absolute Deviation (MAD)2
Skewness0.9542908491
Sum1941
Variance8.077061151
MonotonicityNot monotonic
2023-03-15T20:34:11.488611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0248
33.7%
296
 
13.0%
195
 
12.9%
364
 
8.7%
554
 
7.3%
448
 
6.5%
734
 
4.6%
633
 
4.5%
828
 
3.8%
1020
 
2.7%
Other values (3)16
 
2.2%
ValueCountFrequency (%)
0248
33.7%
195
 
12.9%
296
 
13.0%
364
 
8.7%
448
 
6.5%
554
 
7.3%
5.51
 
0.1%
633
 
4.5%
734
 
4.6%
828
 
3.8%
ValueCountFrequency (%)
1020
 
2.7%
914
 
1.9%
8.51
 
0.1%
828
3.8%
734
4.6%
633
4.5%
5.51
 
0.1%
554
7.3%
448
6.5%
364
8.7%

Music effects
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.4 KiB
Improve
550 
No effect
169 
Worsen
 
17

Length

Max length9
Median length7
Mean length7.436141304
Min length6

Characters and Unicode

Total characters5473
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowImprove
2nd rowImprove
3rd rowNo effect
4th rowImprove
5th rowImprove

Common Values

ValueCountFrequency (%)
Improve550
74.7%
No effect169
 
23.0%
Worsen17
 
2.3%

Length

2023-03-15T20:34:11.619626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-15T20:34:11.772374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
improve550
60.8%
no169
 
18.7%
effect169
 
18.7%
worsen17
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e905
16.5%
o736
13.4%
r567
10.4%
I550
10.0%
m550
10.0%
p550
10.0%
v550
10.0%
f338
 
6.2%
N169
 
3.1%
169
 
3.1%
Other values (5)389
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4568
83.5%
Uppercase Letter736
 
13.4%
Space Separator169
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e905
19.8%
o736
16.1%
r567
12.4%
m550
12.0%
p550
12.0%
v550
12.0%
f338
 
7.4%
c169
 
3.7%
t169
 
3.7%
s17
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
I550
74.7%
N169
 
23.0%
W17
 
2.3%
Space Separator
ValueCountFrequency (%)
169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5304
96.9%
Common169
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e905
17.1%
o736
13.9%
r567
10.7%
I550
10.4%
m550
10.4%
p550
10.4%
v550
10.4%
f338
 
6.4%
N169
 
3.2%
c169
 
3.2%
Other values (4)220
 
4.1%
Common
ValueCountFrequency (%)
169
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e905
16.5%
o736
13.4%
r567
10.4%
I550
10.0%
m550
10.0%
p550
10.0%
v550
10.0%
f338
 
6.2%
N169
 
3.1%
169
 
3.1%
Other values (5)389
7.1%

Interactions

2023-03-15T20:33:57.775594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:50.182486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:51.504164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:52.655519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:53.872320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:55.366980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:56.590562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:57.925609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:50.464168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:51.657175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:52.824560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:54.075585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:55.522964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:56.766572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:58.088619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:50.633608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:51.824187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:52.963569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:54.239597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:55.658469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:56.946584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:58.251043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:50.788619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:51.993203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:53.117092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:54.388610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:55.872993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:57.133615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:58.459056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:50.971031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:52.148215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:53.292106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:54.606624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:56.066943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:57.297628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:58.629924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:51.142665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:52.347228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:53.499783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:54.966721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:56.221954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:57.435637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:58.784937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:51.333678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:52.497614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:53.703306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:55.219969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:56.378133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-15T20:33:57.614583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-15T20:34:11.953433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-03-15T20:34:12.182003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-03-15T20:34:12.393018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-03-15T20:34:12.625931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-03-15T20:34:13.108928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-03-15T20:33:59.114637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-15T20:34:00.399566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgePrimary streaming serviceHours per dayWhile workingInstrumentalistComposerFav genreExploratoryForeign languagesBPMFrequency [Classical]Frequency [Country]Frequency [EDM]Frequency [Folk]Frequency [Gospel]Frequency [Hip hop]Frequency [Jazz]Frequency [K pop]Frequency [Latin]Frequency [Lofi]Frequency [Metal]Frequency [Pop]Frequency [R&B]Frequency [Rap]Frequency [Rock]Frequency [Video game music]AnxietyDepressionInsomniaOCDMusic effects
018.0Spotify3.0YesYesYesLatinYesYes156.0RarelyNeverRarelyNeverNeverSometimesNeverVery frequentlyVery frequentlyRarelyNeverVery frequentlySometimesVery frequentlyNeverSometimes3.00.01.00.0Improve
143.0Pandora1.5YesNoNoRockYesNo119.0SometimesNeverNeverRarelySometimesRarelyVery frequentlyRarelySometimesRarelyNeverSometimesSometimesRarelyVery frequentlyRarely7.02.02.01.0Improve
218.0Spotify4.0NoNoNoVideo game musicNoYes132.0NeverNeverVery frequentlyNeverNeverRarelyRarelyVery frequentlyNeverSometimesSometimesRarelyNeverRarelyRarelyVery frequently7.07.010.02.0No effect
343.0YouTube Music2.5YesNoYesJazzYesYes84.0SometimesNeverNeverRarelySometimesNeverVery frequentlySometimesVery frequentlySometimesNeverSometimesSometimesNeverNeverNever9.07.03.03.0Improve
418.0Spotify4.0YesNoNoR&BYesNo107.0NeverNeverRarelyNeverRarelyVery frequentlyNeverVery frequentlySometimesSometimesNeverSometimesVery frequentlyVery frequentlyNeverRarely7.02.05.09.0Improve
518.0Spotify5.0YesYesYesJazzYesYes86.0RarelySometimesNeverNeverNeverSometimesVery frequentlyVery frequentlyRarelyVery frequentlyRarelyVery frequentlyVery frequentlyVery frequentlyVery frequentlyNever8.08.07.07.0Improve
618.0YouTube Music3.0YesYesNoVideo game musicYesYes66.0SometimesNeverRarelySometimesRarelyRarelySometimesNeverRarelyRarelyRarelyRarelyRarelyNeverNeverSometimes4.08.06.00.0Improve
721.0Spotify1.0YesNoNoK popYesYes95.0NeverNeverRarelyNeverNeverVery frequentlyRarelyVery frequentlyNeverSometimesNeverSometimesSometimesRarelyNeverRarely5.03.05.03.0Improve
819.0Spotify6.0YesNoNoRockNoNo94.0NeverVery frequentlyNeverSometimesNeverNeverNeverNeverNeverNeverVery frequentlyNeverNeverNeverVery frequentlyNever2.00.00.00.0Improve
918.0I do not use a streaming service.1.0YesNoNoR&BYesYes155.0RarelyRarelyRarelyRarelySometimesRarelyRarelyNeverRarelyRarelyNeverSometimesSometimesRarelySometimesSometimes2.02.05.01.0Improve

Last rows

AgePrimary streaming serviceHours per dayWhile workingInstrumentalistComposerFav genreExploratoryForeign languagesBPMFrequency [Classical]Frequency [Country]Frequency [EDM]Frequency [Folk]Frequency [Gospel]Frequency [Hip hop]Frequency [Jazz]Frequency [K pop]Frequency [Latin]Frequency [Lofi]Frequency [Metal]Frequency [Pop]Frequency [R&B]Frequency [Rap]Frequency [Rock]Frequency [Video game music]AnxietyDepressionInsomniaOCDMusic effects
72618.0Apple Music9.5YesNoNoEDMYesNo90.0SometimesRarelyVery frequentlyNeverRarelySometimesSometimesNeverNeverSometimesSometimesRarelySometimesSometimesSometimesSometimes9.08.05.010.0Improve
72726.0YouTube Music1.0YesNoNoMetalYesYes136.0SometimesRarelySometimesVery frequentlyNeverNeverRarelyNeverNeverRarelyVery frequentlyRarelyNeverNeverNeverRarely0.00.00.00.0No effect
72814.0Other streaming service7.0YesYesNoCountryYesNo108.0RarelyVery frequentlySometimesSometimesVery frequentlySometimesNeverNeverNeverRarelySometimesVery frequentlySometimesSometimesVery frequentlyRarely7.03.01.02.0Improve
72921.0I do not use a streaming service.0.5NoNoNoPopYesNo95.0NeverRarelySometimesNeverNeverSometimesRarelySometimesNeverVery frequentlyNeverVery frequentlySometimesSometimesVery frequentlyNever6.02.02.00.0Improve
73021.0Spotify2.0YesNoNoR&BYesYes147.0SometimesNeverSometimesRarelyNeverNeverSometimesVery frequentlyNeverSometimesRarelySometimesVery frequentlySometimesSometimesSometimes7.06.04.06.0Improve
73117.0Spotify2.0YesYesNoRockYesYes120.0Very frequentlyRarelyNeverSometimesNeverSometimesRarelyNeverSometimesRarelyRarelyVery frequentlyNeverRarelyVery frequentlyNever7.06.00.09.0Improve
73218.0Spotify1.0YesYesNoPopYesYes160.0RarelyRarelyNeverNeverNeverNeverRarelyNeverNeverRarelyNeverVery frequentlyNeverNeverSometimesSometimes3.02.02.05.0Improve
73319.0Other streaming service6.0YesNoYesRapYesNo120.0RarelySometimesSometimesRarelyRarelyVery frequentlyRarelyRarelyRarelySometimesRarelySometimesSometimesSometimesRarelyRarely2.02.02.02.0Improve
73419.0Spotify5.0YesYesNoClassicalNoNo170.0Very frequentlyNeverNeverNeverNeverNeverRarelyNeverNeverNeverNeverNeverNeverNeverNeverSometimes2.03.02.01.0Improve
73529.0YouTube Music2.0YesNoNoHip hopYesYes98.0SometimesRarelyVery frequentlySometimesRarelyVery frequentlyVery frequentlySometimesNeverRarelyNeverSometimesVery frequentlyVery frequentlyVery frequentlyRarely2.02.02.05.0Improve